Speech Title: How modeling and simulation helped the European Union to reshape banking regulation
Biography: Prof. Stefano ZEDDA, born in December, 1965, is a professor in Università di Cagliari, Italy. He studies on prudential regulation for banks, simulation models for banks and banking systems stability and its applications, modelling and simulation of financial and energy markets, Basel II and prudential regulation for banks, analysis of banks capital requirements, studies on indicators of banks risks, development of simulation models for estimating banks losses, estimation of banks probability of defaults, studies on the adequacy of Deposit Guarantee Schemes (DGS), and Resolution Funds (RF), computation of banks risk-based contributions to DGS and RF, Cost-Benefit Analysis for new banking regulation. He has done lots of researches in quantitative finance and statistics, mathematical economics, econometrics and its applications, time series analysis, panel data analysis, robust parameter estimation, analysis of financial data.
Abstract: The Global financial crisis started in 2008 has shown the need for important interventions in banking systems regulation and supervision, in order to limit the effects of bank defaults and prevent new financial crises. This discussion has revealed the need for models and methods to assess the effects of these possible interventions, as the lack of data limits the use of the traditional econometric approach. In this aim, simulation models have demonstrated their effectiveness, and the European Commission adopted this approach and included the Systemic Model for Banking Originated Losses (SYMBOL) as a standard tool for the impact assessment of banking regulation reform proposals. This approach was - and is - used to assess the effects of variations in minimum capital requirements, for Deposits Guarantee Schemes dimensioning, to quantify the possible effects of financial crises on public finances stability, for evaluating the possible effects of introducing a bank levy, and more. In this lecture, I’ll present the SYMBOL model, and describe how modeling and simulation resulted to be fundamental for the proper reforming of the European banking regulation and supervision.
Speech Title: Reconstruction of Multidimensional Data on Intelligent Technology and Artificial Intelligence
Biography: Prof. Dariusz Jacek Jakóbczak was born in Koszalin, Poland, on December 30, 1965. He graduated in mathematics (numerical methods and programming) from the University of Gdansk, Poland in 1990. He received the Ph.D. degree in 2007 in computer science from the Polish – Japanese Institute of Information Technology, Warsaw, Poland. From 1991 to 1994 he was a civilian programmer in the High Military School in Koszalin. He was a teacher of mathematics and computer science in the Private Economic School in Koszalin from 1995 to 1999. Since March 1998 he has worked in the Department of Electronics and Computer Science, Koszalin University of Technology, Poland and since October 2007 he has been an Assistant Professor in the Chair of Computer Science and Management in this department. His research interests connect mathematics with computer science and include computer vision, artificial intelligence, shape representation, curve interpolation, contour reconstruction and geometric modeling, numerical methods, probabilistic methods, game theory, operational research and discrete mathematics.
Abstract: Artificial Intelligence is applied for prediction and calculations of unknown values of data or coordinates. Decision makers, academicians, researchers, advanced-level students, technology developers, and government officials will find this text useful in furthering their research exposure to pertinent topics in AI, computer science, numerical analysis or operations research and assisting in furthering their own research efforts in these fields. Proposed method, called Two-Points Smooth Interpolation (TPSI), is the method of 2D curve interpolation and extrapolation using the set of key points (knots or nodes). Nodes can be treated as characteristic points of data for modeling and analyzing. The model of data can be built by choice of probability distribution function and nodes combination. TPSI modeling via nodes combination and parameter γ as probability distribution function enables value anticipation in AI, risk analysis and decision making. Two-dimensional curve is extrapolated and interpolated via nodes combination and different functions as continuous probability distribution functions: polynomial, sine, cosine, tangent, cotangent, logarithm, exponent, arc sin, arc cos, arc tan, arc cot or power function.